Datasets:

kurry
/

institutional-holdings-13f-quarterly

Error code:   DatasetGenerationError
Exception:    ArrowNotImplementedError
Message:      Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 641, in write_table
                  self._build_writer(inferred_schema=pa_table.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 456, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 660, in finalize
                  self._build_writer(self.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 456, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1428, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 989, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1898, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

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End of preview.

US Institutional 13F Holdings Embeddings Dataset

Quarterly snapshots (1980 – 2024) of every U.S.-reported 13F equity holding, plus unsupervised embeddings for:

Institutional managers (investors)
Assets (PERMCO-level equities)

Dataset Description

13F filings are quarterly disclosures to the U.S. SEC by institutions managing ≥ $100 million.
For each quarter (1980 Q1 → 2024 Q3) the dataset provides:

Holdings Table – exact positions with shares, price, and market value
Investor Embeddings – 32-dim vectors from log-dollar-weighted Truncated SVD
Asset Embeddings – companion vectors for the equity universe
Metadata – manager IDs, asset IDs, timestamps, etc.

Directory layout (one folder per quarter):

quarter/
├── holdings/             # parquet/arrow; full table
├── investor_embeddings/  # numpy/arrow; one row per manager
├── asset_embeddings/     # numpy/arrow; one row per PERMCO
└── dataset_dict.json     # Hugging Face DatasetDict metadata

Data Structure

Holdings table (core fields)

Field	Type	Description
`mgrno`	string	Institutional manager ID (SEC)
`permco`	string	Permanent company identifier
`fdate`	date	Quarter-end report date
`shares`	float	Shares held
`price`	float	Price per share on `fdate`
`dollar_holding`	float	Shares × Price (market value of the position)

Embeddings tables

Field	Type	Description
`mgrno` or `permco`	string	Primary key
`embedding`	float32[n] sequence	32-dimensional vector (size may vary)

Coverage and Distribution

Quarters: 1980 Q1 → 2024 Q3 (179 quarters)
Universe: Every stock appearing in any 13F filing during the window
Rows: Tens of millions of manager-asset-quarter tuples
Embeddings: One vector per active manager and per PERMCO each quarter

Quick load

from datasets import DatasetDict

ds = DatasetDict.load_from_disk("institutional-holdings-13f-quarterly/2016Q3")
print(ds["holdings"][0])
print(ds["investor_embeddings"][0])
print(ds["asset_embeddings"][0])

Typical Usage

Alpha/return prediction, manager similarity, clustering
Long-run studies of institutional ownership dynamics
Panel regressions (quarterly frequency)

# Load a single quarter
ds = DatasetDict.load_from_disk("institutional-holdings-13f-quarterly/2020Q4")
print(ds["investor_embeddings"][0]["embedding"][:8])

# Iterate over all quarters
import os
root = "institutional-holdings-13f-quarterly"
for q in sorted(p for p in os.listdir(root) if "Q" in p):
    ds = DatasetDict.load_from_disk(f"{root}/{q}")
    # process ds["holdings"], ds["investor_embeddings"], ds["asset_embeddings"]

Data Splits

Each quarterly folder is a Hugging Face DatasetDict containing:

holdings
investor_embeddings
asset_embeddings

Identifier Definitions

PERMCO – company-level identifier (stable through ticker/name changes)
PERMNO – security-level identifier (stable through symbol/CUSIP changes)

Processing Pipeline

Parse raw 13F text filings.
Map CUSIPs → PERMCOs.
Aggregate shares and price to compute market value.
Compute log-dollar weight
```
w = log(1 + dollar_holding)
```
Build manager-by-asset matrix M with elements w.
Apply Truncated SVD, keep top k = 32 factors (or ≤ rank).
```
M  ≈  U  Σ  Vᵀ
```
- Rows of U → manager embeddings
- Rows of V → asset embeddings

Licensing & Limitations

License: MIT
Intended use: Research & education
Note: Source 13F filings are public; mappings were derived from public data.

Citation

@dataset{kurry2025institutionalholdings13f,
  author    = {Kurry},
  title     = {US Institutional 13F Holdings Embeddings Dataset},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/kurry/institutional-holdings-13f}
}

Quick start

from datasets import DatasetDict
ds = DatasetDict.load_from_disk("institutional-holdings-13f-quarterly/2010Q2")

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